Bayesian updating of hydroelectric turbine fatigue reliability

Abstract

In fatigue design, uncertainties that exist in material, environment, and loading could arise due to manufacturing processes and changing with environment condition. Therefore because of the lack of information and cost of inspection, updating the fatigue model variables to decrease the uncertainties is necessary. In this study, Paris model is used to model the crack growth rate for hydroelectric turbine runner. We applied the Bayesian method to construct the posterior distribution. After constructing the posterior distribution, we update it by Bayesian updating approach. This method is one of the useful methods to decrease the uncertainty of variables at each loading cycle to construct precise prior distribution. The results of updating applied to Kitagawa-Takahashi limit state diagram. After modeling the proper limit state, we apply First Order Reliability Method (FORM) and Monte-Carlo Simulation (MCS) method to calculate the reliability index. In This study all of the procedures that mentioned are described, also we could see the results of effects of prior knowledge and select the distribution to analysis of reliability index. This study follows the (Gagnon, Tahan et al. 2013) research with aim of updating the fatigue reliability amount on hydroelectric turbine runner by Bayesian method.

Date	2 Feb 2016
Original language	American English
Awarding Institution	École de technologie supérieure
Supervisor	Antoine Tahan (Supervisor) & Martin Gagnon (Co-supervisor)